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cs.LG2024
Differentiable Causal Discovery For Latent Hierarchical Causal Models
Parjanya Prashant, Ignavier Ng, Kun Zhang +1
Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterati…
cs.LG2024
Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders
Parjanya Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro +1
We consider the task of out-of-distribution (OOD) generalization, where the distribution shift is due to an unobserved confounder () affecting both the covariates () and the…